Method, apparatus, and system for detecting a physical divider on a road segment

ABSTRACT

An approach is provided for detecting a presence of a physical divider on a road segment. The approach, for example, involves determining a number of positive observations of the physical divider on the road segment based on sensor data collected from one or more vehicles traveling the road segment. The approach also involves determining a number of negative observations of the physical divider on the road segment based on the sensor data. The approach further involves calculating a probability of the presence of the physical divider based on the number of the positive observations and the number of the negative observations. The approach further involves updating map data for the road segment to indicate the presence of the physical divider based on determining that the probability of the presence of the physical divider is greater than a threshold value.

BACKGROUND

Providing environmental awareness for vehicle safety, particularly inautonomous driving, has been a primary concern for automobilemanufacturers and related service providers. For example, knowingwhether physical dividers (e.g., structural separators) exist betweentravel lanes of a road segment can be an indicator that there is lesspotential for inter-lane accidents or collisions. Mapping these physicaldividers, however, has historically been resource-intensive.Accordingly, service providers face significant technical challenges tomore efficiently detect and map physical dividers on road segments.

SOME EXAMPLE EMBODIMENTS

Therefore, there is a need for detecting a physical divider on a roadsegment.

According to one embodiment, a computer-implemented method for detectinga presence of a physical divider on a road segment comprises determininga number of positive observations of the physical divider on the roadsegment based on sensor data collected from one or more vehiclestraveling the road segment. The method also comprises determining anumber of negative observations of the physical divider on the roadsegment based on the sensor data. The method further comprisescalculating a probability of the presence of the physical divider basedon the number of the positive observations and the number of thenegative observations. The method further comprises updating map datafor the road segment to indicate the presence of the physical dividerbased on determining that the probability of the presence of thephysical divider is greater than a threshold value.

According to another embodiment, an apparatus for detecting a presenceof a physical divider on a road segment comprises at least oneprocessor, and at least one memory including computer program code forone or more computer programs, the at least one memory and the computerprogram code configured to, with the at least one processor, cause, atleast in part, the apparatus to determine a number of positiveobservations of the physical divider on the road segment based on sensordata collected from one or more vehicles traveling the road segment. Theapparatus is also caused to determine a number of negative observationsof the physical divider on the road segment based on the sensor data.The apparatus is further caused to calculate a probability of thepresence of the physical divider based on the number of the positiveobservations and the number of the negative observations. The apparatusis further caused to update map data for the road segment to indicatethe presence of the physical divider based on determining that theprobability of the presence of the physical divider is greater than athreshold value.

According to another embodiment, a non-transitory computer-readablestorage medium for detecting a presence of a physical divider on a roadsegment carries one or more sequences of one or more instructions which,when executed by one or more processors, cause, at least in part, anapparatus to determine a number of positive observations of the physicaldivider on the road segment based on sensor data collected from one ormore vehicles traveling the road segment. The apparatus is also causedto determine a number of negative observations of the physical divideron the road segment based on the sensor data. The apparatus is furthercaused to calculate a probability of the presence of the physicaldivider based on the number of the positive observations and the numberof the negative observations. The apparatus is further caused to updatemap data for the road segment to indicate the presence of the physicaldivider based on determining that the probability of the presence of thephysical divider is greater than a threshold value.

According to another embodiment, an apparatus for detecting a presenceof a physical divider on a road segment comprises means for determininga number of positive observations of the physical divider on the roadsegment based on sensor data collected from one or more vehiclestraveling the road segment. The apparatus also comprises means fordetermining a number of negative observations of the physical divider onthe road segment based on the sensor data. The apparatus furthercomprises means for calculating a probability of the presence of thephysical divider based on the number of the positive observations andthe number of the negative observations. The apparatus further comprisesmeans for updating map data for the road segment to indicate thepresence of the physical divider based on determining that theprobability of the presence of the physical divider is greater than athreshold value.

In addition, for various example embodiments of the invention, thefollowing is applicable: a method comprising facilitating a processingof and/or processing (1) data and/or (2) information and/or (3) at leastone signal, the (1) data and/or (2) information and/or (3) at least onesignal based, at least in part, on (or derived at least in part from)any one or any combination of methods (or processes) disclosed in thisapplication as relevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating access to at least oneinterface configured to allow access to at least one service, the atleast one service configured to perform any one or any combination ofnetwork or service provider methods (or processes) disclosed in thisapplication.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising facilitating creating and/orfacilitating modifying (1) at least one device user interface elementand/or (2) at least one device user interface functionality, the (1) atleast one device user interface element and/or (2) at least one deviceuser interface functionality based, at least in part, on data and/orinformation resulting from one or any combination of methods orprocesses disclosed in this application as relevant to any embodiment ofthe invention, and/or at least one signal resulting from one or anycombination of methods (or processes) disclosed in this application asrelevant to any embodiment of the invention.

For various example embodiments of the invention, the following is alsoapplicable: a method comprising creating and/or modifying (1) at leastone device user interface element and/or (2) at least one device userinterface functionality, the (1) at least one device user interfaceelement and/or (2) at least one device user interface functionalitybased at least in part on data and/or information resulting from one orany combination of methods (or processes) disclosed in this applicationas relevant to any embodiment of the invention, and/or at least onesignal resulting from one or any combination of methods (or processes)disclosed in this application as relevant to any embodiment of theinvention.

In various example embodiments, the methods (or processes) can beaccomplished on the service provider side or on the mobile device sideor in any shared way between service provider and mobile device withactions being performed on both sides.

For various example embodiments, the following is applicable: Anapparatus comprising means for performing a method of the claims.

Still other aspects, features, and advantages of the invention arereadily apparent from the following detailed description, simply byillustrating a number of particular embodiments and implementations,including the best mode contemplated for carrying out the invention. Theinvention is also capable of other and different embodiments, and itsseveral details can be modified in various obvious respects, all withoutdeparting from the spirit and scope of the invention. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, andnot by way of limitation, in the figures of the accompanying drawings:

FIG. 1 is a diagram of a system capable of detecting a presence of aphysical divider on a road segment, according to one embodiment;

FIG. 2 is a diagram illustrating examples of physical dividers,according to one embodiment;

FIG. 3 is a diagram illustrating a process for creating a physicaldivider overlay for segments of a road, according to one embodiment;

FIG. 4 is a diagram of the components of a physical divider platform,according to one embodiment;

FIG. 5 is a flowchart of a process for detecting a physical divider on aroad segment, according to one embodiment;

FIG. 6 is a diagram illustrating a graph of example physical dividerprobabilities, according to one embodiment;

FIG. 7 is a flowchart of a process for providing machine learning ofphysical dividers, according to one embodiment;

FIG. 8 is a diagram illustrating an example of a vehicle equipped withsensors to support machine learning of physical dividers, according toone embodiment;

FIG. 9 is a flowchart of a process for predicting physical dividersusing a trained machine learning model, according to one embodiment;

FIGS. 10A and 10B are diagrams of example architectures for providingmachine learning of physical dividers, according to one embodiment;

FIGS. 11A and 11B are diagrams of example user interfaces based onphysical dividers predicted by machine learning, according to onembodiment;

FIG. 12 is a diagram of a geographic database, according to oneembodiment;

FIG. 13 is a diagram of hardware that can be used to implement anembodiment;

FIG. 14 is a diagram of a chip set that can be used to implement anembodiment; and

FIG. 15 is a diagram of a mobile terminal (e.g., handset or vehicle orpart thereof) that can be used to implement an embodiment.

DESCRIPTION OF SOME EMBODIMENTS

Examples of a method, apparatus, and computer program for providingmachine learning of physical dividers using map data and/or vehiclesensor data are disclosed. In the following description, for thepurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of the embodiments of theinvention. It is apparent, however, to one skilled in the art that theembodiments of the invention may be practiced without these specificdetails or with an equivalent arrangement. In other instances,well-known structures and devices are shown in block diagram form inorder to avoid unnecessarily obscuring the embodiments of the invention.

FIG. 1 is a diagram of a system 100 capable of providing machinelearning of physical dividers, according to one embodiment. Havingknowledge of whether physical dividers are present or absent on a roadsegment can provide important situational awareness and improved safetyto vehicles, particularly autonomous vehicles that operate with reducedor no human driver input. In other words, an understanding of whereother cars may potentially be and what they might do is important for anautonomous vehicle to safely plan a route. For example, as shown in FIG.1, a road 101 may support bi-directional traffic with a first vehicle103 traveling in one direction and a second vehicle 105 traveling in theopposite direction. In this example, a first physical divider 107 a ispresent between the vehicle travel lanes of the road 101, and a secondphysical divider 107 b is present between the one of the vehicle travellanes and a pedestrian lane 109 adjacent to the road 101 (alsocollectively referred to as physical dividers 107). The presence of thephysical dividers 107 can improve safety by reducing the probability ofoncoming traffic collisions, or collisions with pedestrians or othernon-vehicular traffic.

In one embodiment, a physical divider 107 is a structural separator thatis a fixed roadside or median entity that prevents vehicles and/orpedestrians traveling in one lane from crossing into or accessing otherlanes of the road (and/or from crossing between different traffic flowdirections). The physical divider 107 can be, for instance, a physicalbarrier or provide enough physical space or clearance between differentlanes or traffic flow directions so that potential cross-over traffic isminimized or prevented. FIG. 2 is a diagram illustrating examples ofphysical dividers. As shown, a physical divider can include, but is notlimited to, (1) a solid wall 201 (e.g., a concrete barrier), (2) amedian 203 that is sufficiently wide to separate reduce potentialcrossover traffic to a threshold probability, (3) a row of columns 205,(4) a row of trees 207, etc. It is noted that, in one embodiment, aphysical divider 107 can be made of any type material or constructionprovided that it reduces, minimizes, or prevents potential crossoverbetween travel lanes or traffic flow directions.

Because of the diversity of physical dividers 107 and the variability oftheir installation along roadways, mapping the presence or absence ofphysical dividers 107 historically has been a resource-intensive effort,typically employing data collection vehicles to travel the roads forhuman observers to manually annotate or physical dividers in a map data(e.g., a geographic database 111). As a result, map providers havetraditionally been only able to map a small percentage of the physicaldividers 107. Therefore, enabling a less-resource intensive and moreautomated process for detecting physical dividers 107 presents asignificant technical problem. Moreover, when detailed mapping ofphysical dividers 107 may be unavailable, the vehicle may have tonavigate using real-time sensing of physical dividers 107. Therefore,the technical challenges also include enabling a real-time or nearreal-time mapping or detection of physical dividers 107.

To address this problem, a system 100 (e.g., via a physical dividerplatform 113) of FIG. 1 introduces a capability to detect a presence orabsence of a physical divider 107 on a given segment of a road 101(e.g., a 5-meter segment of the road 101) by positive and negativeobservations of physical dividers reported by vehicles 103 traveling onthe segment of the road 101. In one embodiment, as the vehicles 103travel on the road 101, they collect sensor data to determine whethereach individual vehicle 103 detects a possible presence of a physicaldivider 107 on the road segment. This results in producing a stream ofphysical divider (PD) signals that indicate whether there is a positiveobservation of the physical divider 107 (e.g., “PD ON” signal indicatingthat the reporting vehicle 103 has detected a physical divider 107using, for instance, its sensor data) or there is a negative observation(e.g., “PD OFF” signal indicating that there the reporting vehicle 103has not detected a physical divider 107 using, for instance, its sensordata). In one embodiment, the PD signal stream can be associated with apath or probe trajectory (e.g., location trace data collected by one ormore location sensors of the vehicle 103) so that the signals can be mapmatched or correlated to a position in the road segment. However,because each PD signal stream represents the observations or just onecorresponding vehicle 103, the system 100 may sometimes receiveconflicting data where one PD signal stream from a first vehicle 103shows a positive observation while another PD signal stream from asecond vehicle 105 shows a negative observation for the same roadsegment. This conflict or data variability can be due to any number ofreasons including but not limited to differences in sensor sensitivity,differences environmental conditions at the time of each observation,random signal variability, etc. Regardless of the cause, this potentialfor conflicting physical divider data creates a significant technicalchallenge for the system 100 to overcome to accurately determine thepresence or absence of a physical divider 107.

To address this additional problem, the physical divider platform 113aggregates PD signal streams or equivalent sensor data (e.g., positiveand negative physical divider observations) from many different vehicles103 for each road segment of interest (e.g., each 5-meter or other sizedsegment of a road 101 mapped in the geographic database 111). In oneembodiment, at least one vehicle (e.g., vehicle 103) can include aphysical divider module 119 for performing one or more functionsassociated detecting physical dividers alone or in combination with thephysical divider platform 113. The physical divider platform 113 canthen calculate a probability that a physical divider 107 exists on theroad segment based on the positive and negative observations determinedfrom the PD signal streams (e.g., by evaluating the numbers of positiveand negative observations). The system 100 can then flag the roadsegment in the digital map (e.g., in the geographic database 111) ashaving a physical divider 107 if the calculated probability is above athreshold value. In one embodiment, the physical divider platform 113can use ground truth data (e.g., annotated data indicating a knownpresence or absence of physical dividers 107 on a variety of referenceor ground truth road segments).

In one embodiment, the physical divider platform 113 can use thedetermined presence or absence of a physical divider 107 (or theprobability of the presence or absence of the physical divider 107) todetermine the probability of other related characteristics such as, butnot limited to: (1) the probability of oncoming or opposite traffic onthe segment of interest (OPPO) (e.g., if there is no physical divider107 between opposite traffic flows on the segment, the probability ofoncoming traffic or a collision with oncoming traffic can be higher);and (2) the probability of the presence of vulnerable road users (VRU)(e.g., if there is no physical divider 107 between vehicular andnon-vehicular traffic, then there is a greater possibility of a possiblecollection with VRU's, e.g., pedestrians, bicyclists, etc.). In oneembodiment, the physical divider platform 113 can predict theprobability of a physical divider 107, the probability threshold forclassifying whether positive/negative observations indicate a detectedphysical divider 107, and/or any of the other characteristics that arerelated to or associated with the presence/absence of a physical divider107 directly using a trained machine learning model 115 (e.g., RandomForest, Decision Tree, Neural Net, or equivalent).

In yet another embodiment, the physical divider platform 113 can use arule-based approach applied on the predicted presence/absence of aphysical divider 107 to predict the other characteristics. For example,if there is a no physical divider 107 predicted on the segment and thesegment supports bi-directional traffic (e.g., as determined from mapdata stored in the geographic database 111), then the physical dividerplatform 113 can also predict that OPPO and VRU probabilities are high.It is noted that this rule is provided by way of illustration and not asa limitation. It is contemplated that the physical divider platform 113can use any equivalent process or means to determine OPPO, VRU, and/orany other related characteristics from a predicted presence/absence of aphysical divider 107 on a segment of interest.

In one embodiment, the physical divider platform 113 segments each ofroad represented in a map database (e.g., the geographic database 111)into segments of a predetermined length (e.g., 5-meter segments). Then,the physical divider platform 113 can aggregate PD signal or othersensor data from vehicles on the respective segments to make physicaldivider predictions for each segment of the road separately orindependently. FIG. 3 is a diagram illustrating an example process forcreating a physical divider overlay 301 for segments of a road 303,according to one embodiment. In one embodiment, the physical divideroverlay 301 is a data structure that can be associated with thegeographic database 111. The physical divider overlay 301 stores, forinstance, data records indicating a presence/absence of a physicaldivider 107 determined according to the embodiments described herein,associated thresholds, OPPO, VRU, and/or other related attributes inassociation with corresponding segments of the road 303.

As shown, the physical divider platform 113 segments the road 303 intosegments 305 a-305 f (also collectively referred to as segments 305).The length of each segment provides for a corresponding level ofgranularity of the detected physical dividers. For example, a defaultlength of the segments 305 can be 5-meters so that each segment 305represents a 5-meter long portion of the road 303. In one embodiment,shorter segment lengths can be used if a higher resolution of detectionof the physical divider 307 a-307 b is desired, and longer segmentlengths can be used to reduce memory storage requirements. The physicaldivider platform 113 can then aggregate positive and negativeobservations of physical dividers from the sensor data/PD signals fromthe reporting vehicles (e.g., vehicles 103 and 105) as the vehiclestraverse each segment 305 a-305 f of the road 303.

In one embodiment, based on the collected positive and negativeobservations of physical dividers for each of the road segments 305a-305 f, the physical divider platform 113 calculates the probability ofthe presence or absence of the physical dividers 307 a-307 b. Thephysical divider platform 113 can then compare the calculatedprobabilities against a threshold value to output a presence (e.g.,probability >threshold) or absence (e.g., probability <threshold) ofrespective physical divider for each segment 305 to store in thephysical divider overlay 301 (e.g., update map data to indicatepresence/absence of a physical divider or a road segment). Table 1 belowillustrates an example of the physical divider determinations made forroad segments 305 a-305 f with respect to physical dividers 307 a and307 b (e.g., occurring on different sides of the road 303 and evaluatedindependently):

TABLE 1 Segment Physical Divider 307a Physical Divider 307b 305a PresentPresent 305b Present Present 305c Present Present 305d Present Present305e Absent Present 305f Absent Present

In one embodiment, the predicted physical divider 107 can then be usedto determine how to operate an autonomous vehicle. For example, if aphysical divider 107 is predicted to be present based on positive andnegative observations of a physical divider on a road segment (e.g.,because no previously determined ground truth data on physical dividersis available for the road segment), then a more autonomous operation ofthe vehicle can be disabled, and the driver is expected to drive in moreof a manual mode (e.g., requiring the driver to hold the steering wheelas the vehicle operates otherwise in autonomous mode, or to disable someor all autonomous operations). In one embodiment, other use casesinclude updating the physical divider overlay 301 and/or geographicdatabase 111 with the newly detected physical dividers. It is noted thatthese uses cases are provided by way of illustration and not aslimitations. Accordingly, it is contemplated that the determinedphysical divider information and/or related attributes (e.g., OPPO, VRU,etc.), can be used for any other use case, application, and/or service.

FIG. 4 is a diagram of the components of a physical divider platform 113and/or physical divider module 119, according to one embodiment. By wayof example, the physical divider platform 113 and/or physical dividermodule 119 includes one or more components for detecting a presence of aphysical divider on a road segment according to the various embodimentsdescribed herein. It is contemplated that the functions of thesecomponents may be combined or performed by other components ofequivalent functionality. In this embodiment, the physical dividerplatform 113 and/or physical divider module 119 include a sensor dataingestion module 401, a map data module 403, a divider classificationmodule 405, a data publication module 407, and a vehicle control module409. The above presented modules and components of the physical dividerplatform 113 and/or physical divider module 119 can be implemented inhardware, firmware, software, or a combination thereof. Though depictedas separate entities in FIG. 1, it is contemplated that the physicaldivider platform 113 and/or physical divider module 119 may beimplemented as a module of any of the components of the system 100(e.g., a component of the vehicle 103, services platform 121, services123 a-123 n (also collectively referred to as services 123), etc.). Inanother embodiment, one or more of the modules 401-409 may beimplemented as a cloud-based service, local service, native application,or combination thereof. The functions of the physical divider platform113, physical divider module 119, and modules 401-409 are discussed withrespect to FIGS. 5-11B below.

FIG. 5 is a flowchart of a process for detecting a physical divider on aroad segment, according to one embodiment. In various embodiments, thephysical divider platform 113, physical divider module 119, and/or anyof the modules 401-409 may perform one or more portions of the process500 and may be implemented in, for instance, a chip set including aprocessor and a memory as shown in FIG. 14. As such, physical dividerplatform 113, physical divider module 119, and/or any of the modules401-409 can provide means for accomplishing various parts of the process500, as well as means for accomplishing embodiments of other processesdescribed herein in conjunction with other components of the system 100.Although the process 500 is illustrated and described as a sequence ofsteps, its contemplated that various embodiments of the process 500 maybe performed in any order or combination and need not include all of theillustrated steps.

In step 501, the sensor data ingestion module 401 determines a number ofpositive observations of a physical divider on a road segment and anumber of negative observations of the physical divider on the roadsegment. As noted above, the physical divider platform 113 can segment aroad into discrete segments of a predetermined length (e.g., 5-meters)to facilitate processing and representation in the geographic database111. In this case, for each segment of road, the sensor data ingestionmodule 401 extracts raw data collected from vehicle sensors (e.g.,camera, radar, LiDAR, etc.) of vehicles traveling on the segment ofinterest. The raw data, for instance, can contain multiple traces orprobe trajectories (e.g., a sequence of time-stamped location pointsdescribing a traveled path) from multiple reporting vehicles 103). Theprobe trajectories further include PD signal stream data (e.g., physicaldivider flags—PD ON and PD OFF—sequenced in time and correlated to therespective probe trajectories) for each of the multiple reportingvehicles 103. The PD signal streams, for instance, represent individualphysical divider predictions based on the sensor data of each of thereporting vehicles 103. In one embodiment, these individual predictionscan be computed as described with respect to the machine learningprocess of FIG. 7 (described below) or equivalent.

In one embodiment, the traces or probe trajectories of the sensor dataare map matched against the digital map of the geographic database 111to associate them with map road links. In this way, the sensor dataingestion module 401 can query the map matched data for the sensor dataor traces corresponding to each road segment of interest for processingto detect physical dividers. In one embodiment, the sensor data can bemap matched using a path-based map matcher. The reporting vehicle'sdirection of travel can be inferred from the time stamp and locationpoints (e.g., GPS points) in the traces.

In embodiments where the traces include PD signal streams, the dataingestion module can extract the physical divider flags (e.g., PD ON andPD OFF) for each 5-meter segment of road to determine positive ornegative observations on a segment by segment basis. For example, todetermine positive observations of a physical divider, the dataingestion module 401 extracts road segments on which physical dividershave been observed (positive observations, i.e., PD ON). The followingpairs of messages or equivalent in the sensor data are then used toidentify positive physical divider observations in the sensor datacollected from vehicles that traveled the road segment:

-   -   (BEGIN/INITIAL, is Physical Divider=True)->(BEGIN/INITIAL,        isPhysicalDivider=False)    -   (BEGIN/INITIAL, is Physical Divider=True)->(END/CANCEL,        isPhysicalDivider=False)

In other words, the positive observations are extracted by segmenting aportion of the plurality of probe trajectories that corresponds to theroad segment of interest. The portion to segment begins with a positiveflag (e.g., a flag indicating a positive observation or a beginning or apositive observation such as “BEGIN/INITIAL, is Physical Divider=True”)and ends with a negative flag (e.g., a flag indicating an end of thepositive observation such as “BEGIN/INITIAL, is Physical Divider=True”or “END/CANCEL, isPhysicalDivider=False” as determined from theplurality of physical divider flags in the trajectories).

The process for determining negative observations in the sensor data issimilar. For example, the data ingestion module 401 extracts roadsegments on which the lack of physical dividers has been explicitlyidentified (e.g., with flags) or implicitly implied (e.g., paths where avehicle passed but made no physical divider report) to representnegative observations. In one embodiment, the data ingestion module 401uses the following pairs of messages or equivalent to extract explicitlyidentified negative physical divider observations for the sensor data:

-   -   (BEGIN/INITIAL, is Physical Divider=False)->(BEGIN/INITIAL,        isPhysicalDivider=True)    -   (BEGIN/INITIAL, is Physical Divider=False)->(END/CANCEL,        isPhysicalDivider=False)

In other words, the negative observations are extracted by segmenting aportion of the plurality of probe trajectories that corresponds to theroad segment. The portion begins, for instance, with a negative flag(e.g., a flag indicating a beginning of the negative observation such as“BEGIN/INITIAL, is Physical Divider=False” as determined from theplurality of physical divider flags in the trajectories) and ends with anegative positive divider flag as determined from the plurality ofphysical divider flags (e.g., a flag indicating an end of the positiveobservation such as “BEGIN/INITIAL, is Physical Divider=False” or“END/CANCEL, isPhysicalDivider=False” as determined from the pluralityof physical divider flags in the trajectories).

In one embodiment, the reporting vehicles are configured to reportpositive or negative observations only when they are in a lane adjacentto the physical divider (e.g., left-most lane when the physical divideris on the left side of the road or the right-most lane when the physicaldivider is on the right side of the road). Alternatively, if thevehicles are configured to report observations regardless of laneposition and if the such filtering is desired by the by physical dividerplatform 113, the data ingestion module 401 can filter the positiveobservations, the negative observations, or a combination thereof basedon determining that the one or more vehicles are farther than a distancethreshold from the physical divider on the road segment. By way ofexample, the distance threshold can be based on the physical dimensionsof a travel lane adjacent to the physical divider or any otherpre-determined distance.

After obtaining the positive and negative observations of the physicaldivider from the sensor data as described above (e.g., filtered orunfiltered by lane distance), the data ingestion module 401 candetermine a total count or number of positive and negative observationsfor the given road segment.

In step 503, the divider classification module 405 then calculates aprobability of the presence of the physical divider or a given roadsegment based on the number of the positive observations and the numberof the negative observations. It is contemplated that the dividerclassification module 405 can use any function and/or weighting for thepositive and/or negative observations physical divider observations tocalculate the probability. On example function includes but is notlimited to calculated based on a ratio of the number of positiveobservations to a total number of the positive observations and thenegative observations as follows:

PD probability=positive PD count/(negative PD count+positive PD count)

where PD probability is the probability that a physical divider existsor is present on a road segment, positive PD count is the number ofpositive physical divider observations determined from the sensor datafor the road segment, and negative PD count is the number of negativephysical divider observations for the road segment.

In one embodiment, if PD probability >threshold (step 505), then dividerclassification module 405 determines that a physical divider is presentfor that road segment (e.g., 5-meter segment) and updates the map data(e.g., records in the geographic database 111) for the road segment toindicate the presence of the physical divider (step 507). Effectively,this update implies that the probability for opposing traffic on thatroad segment is low. Based on this data, an autonomous vehicle travelingon that segment can switch to self-driving mode.

In one embodiment, the divider classification module 405 can calculatethe threshold value based on ground truth data indicating a knownpresence, a known absence, or a combination thereof of a plurality ofreference physical dividers on a plurality of reference road segments.For example, the divider classification module 405 can process theground truth data to calculate PD probabilities for various 5-meter roadsegments as shown in the graph 601 in FIG. 6. In this example, the graph601 illustrates the percentage of all computed road segments that fallunder bins for each calculated PD probability with peaks at the bin fora probability of 0 (e.g., segments with 0 positive count—PD OFF only),and the bin for a probability of 1 (e.g., segments with only positivecounts—PD ON only). However, the inner bins also contain entries whichcan suggest that a carefully selected threshold that balances thetradeoff between correct and incorrect predictions can be determined.Table 1 below illustrates an example of different possible cut-offprobabilities against correct predictions. It is noted that theillustrated numbers are provided for illustration and that actualnumbers can be generated from analysis of ground truth data as describedabove. A system user can then select a probability threshold based on atarget prediction accuracy.

TABLE 1 Cut-Off Probability (e.g., Threshold) Percent CorrectPredictions 0 50% <0.1 80% <0.2 85% <0.3 90% <0.4 85% <0.5 80% <0.6 75%<0.7 70% <0.8 65% <0.9 60% 1 50%

In one embodiment, the process 500 of FIG. 5 above uses positive andnegative physical divider observations generated from PD signal streamscollected various vehicles. As previously described the PD signalstreams include per vehicle predictions of PD ON (e.g., physical dividerdetected by a reporting vehicle 103) or PD OFF (e.g., physical dividernot detected). It is contemplated that the embodiments of the process500 can use any process for generating the PD signal streams includingbut not limited to the process as described with respect to FIG. 7. FIG.7 is a flowchart of an example process for providing machine learning ofphysical dividers that can be used for generating a PD signal stream,according to one embodiment. In various embodiments, the physicaldivider platform 113, physical divider module 119, and/or any of themodules 401-409 may perform one or more portions of the process 700 andmay be implemented in, for instance, a chip set including a processorand a memory as shown in FIG. 14. As such, physical divider platform113, physical divider module 119, and/or any of the modules 401-409 canprovide means for accomplishing various parts of the process 700, aswell as means for accomplishing embodiments of other processes describedherein in conjunction with other components of the system 100. Althoughthe process 700 is illustrated and described as a sequence of steps, itscontemplated that various embodiments of the process 700 may beperformed in any order or combination and need not include all of theillustrated steps. More specifically, the process 700 describes aprocess for collecting map and/or vehicular sensor data to train amachine learning model (e.g., machine learning model 115) to predictphysical dividers for a given road segment.

In step 701, the physical divider platform 113 can use any combinationof map and/or vehicular sensor data to create a training data set fortraining the machine learning model 115. In one embodiment, thecomposition of the training data set can be based on a target level ofprediction accuracy. For example, retrieving both map data and sensordata can potentially provide for increase predictive accuracy overeither type of data individually. However, when the target predictiveaccuracy can be achieved by using map data or sensor data alone, thephysical divider platform 113 can reduce the resource-burden associatedwith having to collect both datasets.

In one embodiment, the sensor data ingestion module 401 can be used toretrieve vehicular sensor data, and the map data module 403 can be usedto retrieve map data for given segment of a road. As noted above, thephysical divider platform 113 can segment a road into discrete segmentsof a predetermined length (e.g., 5-meters) to facilitate processing. Inthis case, for each segment of road, the sensor data ingestion module401 extracts raw data collected from vehicle sensors (e.g., camera,radar, LiDAR, etc.) of vehicles traveling on the segment of interest.For example, the raw sensor data can include, but is not limited, to across-sensor consistency distribution (e.g., minimum, maximum, mean,standard deviation, or other statistical parameter of the distributioncan be used), a physical divider distance distribution (e.g., alsominimum, maximum, mean, standard deviation, or other statisticalparameter of the distribution can be used), a height of the physicaldivider 107, a vehicle speed, a physical divider type (e.g., seeexamples of FIG. 3), a physical divider sample point count, or acombination thereof. It is contemplated that any sensed parameter of thevehicle, the physical divider 107, or the road segment (e.g., weather,time of day, visibility, etc.) can be collected as raw sensor data forprocessing by the sensor data ingestion module 401.

FIG. 8 is a diagram illustrating an example of a vehicle 801 equippedwith sensors to support machine learning of physical dividers, accordingto one embodiment. As shown, a vehicle 801 is equipped with a camerasensor 803, a LiDAR sensor 805, and a radar sensor 807. Each of thesesensors 803-807 are capable of sensing the presence of a physicaldivider 107 individually. However, each sensor 803-807 has a respectiveweakness. For example, LiDAR data or camera data of the physical divider107 can be obscured if there an obstruction (e.g., another vehicle)between the vehicle 801 and the physical divider 107. Similarly, radarsignals may pass through the physical divider 107 when it is made orporous material or other material that reflects radar signals poorly,have a low height, etc. These weaknesses can potentially lead to lessreliable or less accurate detection of physical dividers 107.

To address the individual technical weaknesses of the sensors 803-807,the sensor data ingestion module 401, for instance, can performin-vehicle sensor fusion of the physical divider 107 or structuralseparator. In other words, the sensor data ingestion module 401 can usemultiple different sensors 803-807 to determine a consistency ofdetection of the physical divider 107 among the different sensors803-807. For example, with respect to cross-sensor consistency, thesensor data is collected from at least two sensors of the vehicle. Thenthe various sensed characteristics of the detected physical divider 107can be compared (e.g., location from the vehicle 801, detected height ofthe physical divider 107, etc.) for consistency (e.g., by calculating apercent difference or equivalent). In addition, because each of thesensors are capable of sampling multiple times per second or faster, adistribution of the cross-sensor consistency can be determined from thesampling set. In one embodiment, the cross-sensor consistencydistribution can be one parameter retrieved by the sensor data ingestionmodule 401 as feature indicating a consistency of detecting the physicaldivider 107 between each of at least two of the sensors 803-807.

In one embodiment, the sensor data ingestion module 401 can also usesensor data from multiple vehicles traveling on the same road segment todetermine additional attributes or features for machine learning. Forexample, the sensor data ingestion module 401 can process the sensordata from a plurality of vehicles traveling the segment of the road todetermine or calculate a derivative feature. A derivative feature refersto any feature or attribute that can be calculated or processed from theraw data from multiple vehicles (e.g., not directly sensed by a sensor803-807). For example, the derivative feature can include, but is notlimited to, the number of positive observations of the physical divider107 by unique vehicles traveling the road segment. In one embodiment,this number of positive observations can be normalized by the totalnumber of drives or vehicles that passed the given segment.

In an embodiment where the map data is used alone or in combination withthe sensor data, the map data module 403 can retrieve requested map datafor a road segment of interest by performing a location-based query ofthe geographic database 111 or equivalent. By way of example, the mapdata can include any attribute of the road segment or corresponding maplocation stored in the geographic database 111. The retrieved map datacan include, but is not limited to, a functional class, a speed limit, apresence of a road sign (e.g., school zone sign), a bi-directionality ofthe road, a number of lanes, a speed category, a distance to a nearbypoint of interest, or a combination thereof. The map data can alsoinclude the presence of non-vehicular travel lanes on the road segment(e.g., sidewalks, bicycle paths, etc.).

In one embodiment, the sensor data ingestion module 401 can retrievesensor data directly from vehicles with connected communicationscapabilities (e.g., cellular or other wireless communications equippedvehicles) or from an Original Equipment Manufacturer (OEM) provider(e.g., automobile manufacturer) operating an OEM platform (e.g., aservices platform 123) that collects sensor data from vehiclesmanufactured by or otherwise associated with the OEM. The retrieval ofthe sensor data and/or the map data can occur in real-time or nearreal-time, continuously, periodically, according to a schedule, ondemand, etc.

In one embodiment, the sensor data can be provided as location tracedata in which each sensor data sampling point is associated withlocation coordinates of the collection vehicle. The location coordinatescan be determined from location sensors (e.g., GPS/satellite-basedlocation sensors or equivalent) and recorded with the sensor data. Inthis case, the sensor data ingestion module 401 can perform a mapmatching (e.g., using any map matching technique known in the art) ofthe location data of each sensor data sampling point to identify whichroad segment the sensor data sampling point belongs. In other words,each location trace is associated with segments of map road links andtransformed into sensor data observations for a particular segment ofthe road link. For example, the data ingestion module 401 can use apath-based map matching algorithm by calculating the collectionvehicle's direction of travel from the time stamp and GPS points presentin the retrieved sensor data.

After retrieval of the map data, sensor data, and/or derivative feature,the divider classification module 405 can process the data to extract afeature vector comprising the attributes indicated in the map data, thesensor data, the derivative feature, or a combination thereof. Thisfeature vector can then be provided as an input to the machine learningmodel 115. When used for training the machine learning model 115, thefeature vector can be part of a training data set. When used for actualprediction, the feature vector is provided as an input to a trainedmachine learning model 115 for predicting the presence/absence of aphysical divider 107 at a corresponding segment of interest for outputas a PD signal stream of the reporting vehicle. This out can then beused in the embodiments of the process 500 of FIG. 5 described above fordetecting physical dividers based on positive and negative observations.

With respect to the training use case, after creating the feature vectoras described above for inclusion in a training data set, the dividerclassification module 405 retrieves ground truth data about a physicaldivider 107 for the segment of the road (step 703). The ground truthdata, for instance, indicates a true presence or a true absence of thephysical divider 107 on the segment of the road of interest. This groundtruth data can be collected using traditional or equivalent techniques(e.g., manual human annotation of a collected sensor data observation toindicate presence or absence of a physical divider 107 and/or its typeor characteristics). For example, a map service provider can operate afleet of map data collection vehicles that can more sophisticated,accurate, or different types of sensors (e.g., radar, cameras, LiDAR,etc.) than would normally be available in customer vehicles. Asdescribed above, the physical divider is a fixed roadside or a medianstructure that separates different traffic flow directions or types(e.g., vehicular traffic vs. non-vehicular traffic). In one embodiment,only segments for which ground truth data is collected or otherwiseavailable are selected for training the machine learning model.

In one embodiment, when independent ground truth data is not availableor otherwise not used, the divider classification module 405 can use theunderlying sensor data individually to estimate whether a physicaldivider is present or absent on the corresponding road segment. Forexample, image recognition can be performed on camera sensor datacollected from a vehicle traveling the road segment. If imagerecognition results in detecting the presence or absence a physicaldivider in the image data, the results can be used as pseudo-groundtruth data to train the machine learning classifier. In this way, whenoperating with independent ground truth data, map and sensor datacollected from one road segment can be used to train the machinelearning model 115 to predict the presence or absence of physicaldividers on other road segments.

In step 705, the divider classification module 405 processes the mapdata, the sensor, or a combination thereof and the ground truth data totrain the machine learning model 115 to predict the physical dividerusing the map data, the sensor data, or a combination thereof as aninput. As previously discussed, the machine learning model 115 can bebased on any supervised learning model (e.g., Random Forest, DecisionTree, Neural Net, Deep Learning, logistic regression, etc.). Forexample, in the case of a neural network, the machine learning model 115can consist of multiple layers or collections of one or more neurons(e.g., processing units of the neural network) corresponding to afeature or attribute of the input data (e.g., the feature vectorgenerated from the map and/or vehicular sensor data as described above).

During training, the divider classification module 405 uses a learnermodule that feeds feature vectors from the training data set (e.g.,created from map and/or sensor data as described above) into the machinelearning model 115 to compute a predicted matching feature (e.g.,physical divider and/or other related characteristic to be predicted)using an initial set of model parameters. For example, the targetprediction for the machine learning model 115 can be whether there is aphysical divider present for a given segment (e.g., 5-meter segment) ofa road of interest. In one embodiment, the machine learning model 115can also be used to model or predict shape, distance from vehicle to thephysical divider (e.g., from a vehicle reference point such as the rearaxle), and/or other attributes of the physical divider 107 if the groundtruth data contains attributes or feature labels.

In addition or alternatively, the machine learning model 115 can be usedto predict a road characteristic related to the physical divider. Forexample, the road characteristic related to the physical divider caninclude, but is not limited to, a probability of oncoming traffic(OPPO), a presence of vulnerable road users (VRU), or a combinationthereof. In one embodiment, the prediction can be a binary prediction(e.g., physical divider/OPPO/VRU present or physical divider/OPPO/VRUabsent). In another embodiment, prediction can be a probability of thephysical divider/OPPO/VRU being present or absent (e.g., spanning anumerical range from 0 to 1, with 0 being no probability and 1 being thehighest probability).

The learner module then compares the predicted matching probability andthe predicted feature to the ground truth data (e.g., the manuallymarked feature labels) in the training data set for each sensor dataobservation used for training. In addition, the learner module computesan accuracy of the predictions for the initial set of model parameters.If the accuracy or level of performance does not meet a threshold orconfigured level, the learner module incrementally adjusts the modelparameters until the model generates predictions at a desired orconfigured level of accuracy with respect to the manually marked labelsof the ground truth data. In other words, a “trained” machine learningmodel 115 is a model with model parameters adjusted to make accuratepredictions with respect to the training data set and ground truth data.

In step 707, the divider classification module 405 uses the trainedmachine learning model 115 to a generate a PD signal stream for thephysical divider platform 113 to generate a physical divider overlay ofa map representation of a road network using the embodiments of theprocess 500 of FIG. 5 described above. For example, the dividerclassification module 405 can interact with the sensor data ingestionmodule 401 and map data module 403 receive sensor data observations fromOEM providers and/or vehicles traveling in the road network. Theobservations can then be used an input into the trained machine learningmodel 115 as discussed in more detailed below with respect to FIG. 9. Inone embodiment, the physical divider overlay indicates a presence or anabsence of one or more physical dividers in the road network of the maprepresentation. In one embodiment, the physical divider overlay can alsoinclude other data related to a presence or an absence of one or morephysical dividers in the road network of the map representation such asa probability of oncoming traffic (OPPO), a presence of vulnerable roadusers (VRU), or a combination thereof as previously discussed.

In other words, in one embodiment, given the training data above, thephysical divider platform 113 can run a batch process (e.g., every 24hours or any other configured time interval) and extract the featurevectors as described above, and pass the feature vectors to the alreadytrained machine learning model 115. The trained machine learning model115 will output positive and/or negative observations of physicaldividers for a road segment being analyzed.

In one embodiment, the data publication module 407 can then publish thephysical divider overlay in the geographic database 111 or equivalentfor access by end users (e.g., OEMs, vehicles, etc.).

FIG. 9 is a flowchart of a process for predicting physical dividersusing a trained machine learning model, according to one embodiment. Invarious embodiments, the physical divider platform 113, physical dividermodule 119, and/or any of the modules 401-409 may perform one or moreportions of the process 900 and may be implemented in, for instance, achip set including a processor and a memory as shown in FIG. 14. Assuch, physical divider platform 113, physical divider module 119, and/orany of the modules 401-409 can provide means for accomplishing variousparts of the process 900, as well as means for accomplishing embodimentsof other processes described herein in conjunction with other componentsof the system 100. Although the process 900 is illustrated and describedas a sequence of steps, its contemplated that various embodiments of theprocess 900 may be performed in any order or combination and need notinclude all of the illustrated steps. In one embodiment, the process 900provides additional detail regarding the use of the machine learningmodel 115 trained according to the process 700 of FIG. 7 to generate aPD signal stream or equivalent physical divider observations.

In step 901, the sensor data ingestion module 401 and/or the map datamodule 403 collect map data, sensor data, or a combination thereof froma vehicle traveling on a road segment. This step is equivalent to step701 of the process 700 described above. However, in this use case, theroad segment of interest is a road segment for which a prediction of aphysical divider 107 or other related characteristic is requested.

In step 903, the divider classification module 405 processes the mapdata, the sensor data, or a combination thereof using a machine learningmodel to predict a presence or an absence of a potential physicaldivider on the target road segment. In one embodiment, the map data,sensor data, and/or any derivative feature determined therefrom (e.g.,the derivative feature as described above in process 700) are used togenerate a feature vector of the attributes of the collected data as aninput into the trained machine learning model 115. In one embodiment,the machine learning model 115 is trained using training map data,training sensor data, or a combination thereof and ground truth dataregarding a true presence or a true absence of a reference physicaldivider as described above with respect to the process 700.

In step 905, the vehicle control module 409 activates or deactivates anautonomous driving mode of the vehicle based on the predicted presenceor the predicted absence of the physical divider (e.g., predictedlocally via the process 900 or retrieved from the physical divideroverlay created using the process 500 of FIG. 5 from positive andnegative physical divider observations). In addition or alternatively,the vehicle control module 409 can present a notification to the driveror occupant of the vehicle prior to activating or deactivating theautonomous mode. For example, the notification can alert the driver thata change in the autonomous mode will occur shortly (e.g., within aspecified period of time). In another example, the notification canprovide the driver an option to accept or reject the pending change inautonomous driving mode, or select other alternatives (e.g., reroute thevehicle to road segments with physical dividers, etc.). In oneembodiment, the autonomous driving mode is deactivated based on thepredicted presence and activated based on the predicted absence of thephysical divider. By way of example, the vehicle can be an autonomousvehicle or highly assisted driving vehicle that is capable of sensingits environment and navigating within a road network without driver oroccupant input. It is noted that autonomous vehicles and highly assisteddriving vehicles are part of a spectrum of vehicle classifications thatcan span from no automation to fully autonomous operation. For example,the U.S. National Highway Traffic Safety Administration (“NHTSA”) in its“Preliminary Statement of Policy Concerning Automated Vehicles,”published 2013, defines five levels of vehicle automation:

-   -   Level 0 (No-Automation)—“The driver is in complete and sole        control of the primary vehicle controls—brake, steering,        throttle, and motive power—at all times.”;    -   Level 1 (Function-specific Automation)—“Automation at this level        involves one or more specific control functions. Examples        include electronic stability control or pre-charged brakes,        where the vehicle automatically assists with braking to enable        the driver to regain control of the vehicle or stop faster than        possible by acting alone.”;    -   Level 2 (Combined Function Automation)—“This level involves        automation of at least two primary control functions designed to        work in unison to relieve the driver of control of those        functions. An example of combined functions enabling a Level 2        system is adaptive cruise control in combination with lane        centering.”;    -   Level 3 (Limited Self-Driving Automation)—“Vehicles at this        level of automation enable the driver to cede full control of        all safety-critical functions under certain traffic or        environmental conditions and in those conditions to rely heavily        on the vehicle to monitor for changes in those conditions        requiring transition back to driver control. The driver is        expected to be available for occasional control, but with        sufficiently comfortable transition time.”; and    -   Level 4 (Full Self-Driving Automation)—“The vehicle is designed        to perform all safety-critical driving functions and monitor        roadway conditions for an entire trip. Such a design anticipates        that the driver will provide destination or navigation input,        but is not expected to be available for control at any time        during the trip. This includes both occupied and unoccupied        vehicles.”

In one embodiment, the various embodiments described herein areapplicable to vehicles that are classified in any of the levels ofautomation (levels 0-4) discussed above. For example, in the case ofautonomous modes of operation, the vehicle can automatically react todetected physical dividers, OPPO, VRU, etc. (e.g., by automaticallyrerouting, slowing down, etc.). Even in the case of completely manualdriving (e.g., level 0), the vehicle can present an alert ornotification of any detected physical dividers, OPPO, VRU, etc. toprovide greater situational awareness and improve safety for drivers.

In another use case of a physical divider prediction, in addition to orinstead of autonomous vehicle control, the data publication module 407can initiate an update of physical divider overlay of a map databasebased on the predicted presence or the predicted absence of the physicaldivider, OPPO, VRU, etc. on the road segment (step 907). For example, ifthe segment has been previously unmapped, the predicted physicaldivider/OPPO/VRU can be transmitted for possible inclusion in physicaldivider overlay of the geographic database 111. The physical dividerplatform 113 can use any criteria for determining whether a new physicaldivider prediction should be incorporated into an existing physicaldivider overlay. For example, if the report is from a trusted vehicle(e.g., a mapping vehicle operated by a map provider), a singleprediction can be used to update the overlay. If the report is from auser vehicle, the physical divider platform 113 may update the overlayonly if the report meets predetermined criteria (e.g., confirmed by apredetermined number of other user vehicles, has calculated probabilityabove a threshold value, etc.).

FIGS. 10A and 10B are diagrams of example architectures for detectingphysical dividers, according to one embodiment. FIG. 10A illustrates anexample architecture 1001 in which the machine learning model 115 isinstantiated on a network component (e.g., the physical divider platform113). In this way, the processing needed by the machine learning model115 is provided on the server side, where computing resources (e.g.,processing power, memory, storage, etc.) is generally greater than at alocal component (e.g., the vehicle 103).

Under the architecture 1001, an OEM platform 1003 (e.g., operated byautomobile manufacturer) collects sensor data observations from vehiclesas they travel in a road network. The OEM platform 1003 sends theobservations to the physical divider platform 113 (e.g., typicallyoperated by a map or other service provider) for ingestion and archival.The physical divider platform 113 (e.g., where the trained machinelearning model 115 is instantiated) then processes the receivedobservations to predict physical dividers, OPPO, VRU, etc. Thesephysical divider/OPPO/VRU predictions are then fused with map attributeinformation to produce the physical divider overlay 1005. The physicaldivider platform 113 can then publish the physical divider overlay 1005for delivery to the vehicle 103 either directly or through the OEMplatform 1003.

FIG. 10B illustrates an alternative architecture 1021 in which nophysical divider overlay is delivered to the vehicle 103. Instead, atrained machine learning model 115 is instantiated at a local componentor system of a vehicle 103 traveling the road network. In this way, thelocal component uses the machine learning model 115 to provide a localprediction of the physical divider (e.g., physical divider predictions1023 as, for instance, a PD signal stream) based on locally collectedmap and/or sensor data. In one use case, the local prediction of thephysical divider is provided to the physical divider platform 113 as aPD signal stream or equivalent to facilitate detecting a physicaldivider on a road segment according to the embodiments described herein.

As shown, to enable this architecture 1021, the physical dividerplatform 113 trains the machine learning model 115 as previouslydescribed in the process 700. The physical divider platform 113 can thendeliver the trained machine learning model 115 to the vehicle 103 eitherdirectly or through the OEM platform 1025. A local system or componentof the vehicle 103 then executes an instance of the trained machinelearning model 115 to make physical divider/OPPO/VRU predictions locallyat the vehicle 103. In this way, the vehicle is able detect or mapphysical dividers on the segments on which it is traveling when aphysical divider overlay is not available or when the vehicle does nothave communications to network-side components such as the physicaldivider platform 113 as it travels. In one embodiment, as new trainingdata is collected, an updated trained machine learning model 115 can bedelivered to the vehicle 103 as needed, periodically, etc.

FIGS. 11A and 11B are diagrams of example user interfaces based onphysical dividers predicted by machine learning, according to onembodiment. In the example of FIG. 11A, the vehicle 103 is traveling ona road segment that has not been previously mapped for the presence ofany physical dividers between opposite traffic flow lanes. The vehicle103 also is currently operating in autonomous driving mode. As thevehicle 103 approaches the segment, the vehicle sensors (e.g., camera,radar, etc.) collect sensor data. At the same time, the map data (e.g.,functional class, speed category, etc.) about the road segment is alsodetermined. A vehicle system 1101 including trained machine learningmodel 115 (e.g., trained according to the embodiments described herein)then processes the map and sensor data to make a physical dividerprediction, and/or can query the physical divider information from thephysical divider overlay generated as described above. In this example,the machine learning model 115 predicts that there is no physicaldivider on the segment. This prediction then triggers the vehicle system1101 to present an alert message 1103 to indicate that that the vehicle103 is approaching an area with no physical divider and instructs thedriver to take manual control for the segment. In addition, the vehiclesystem 1101 can deactivate the autonomous driving mode (e.g., followinga period of time after presenting a notification such as the alertmessage 1103).

FIG. 11B is a diagram illustrating an example user interface presentinga physical divider overlay, according to one embodiment. As shown, adisplay device 1121 (e.g., connected to a vehicle or personal navigationsystem) presents a mapping display on which a physical divider overlay(e.g., generated according to the various embodiments described herein)is superimposed. In this example, the physical divider overlay includesinformation on which road segments have ground truth mapped physicaldividers (e.g., segments indicated with a solid line 1123) as well asphysical dividers determined from positive and negative physical dividerobservations according to the embodiments described herein and/orpredicted by a trained machine learning model 115 (e.g., segmentsindicated with dashed lines 1125 a and 1125 b). In addition, thephysical overlay, includes data on segments with observed or predictedOPPO (e.g., indicated by shaded area 1127) as well as observed orpredicted VRU (e.g., indicated by shaded area 1129).

Returning to FIG. 1, in one embodiment, the physical divider platform113 has connectivity over a communication network 125 to the servicesplatform 121 (e.g., an OEM platform) that provides one or more services123 (e.g., sensor data collection services). By way of example, theservices 123 may also be other third-party services and include mappingservices, navigation services, travel planning services, notificationservices, social networking services, content (e.g., audio, video,images, etc.) provisioning services, application services, storageservices, contextual information determination services, location-basedservices, information-based services (e.g., weather, news, etc.), etc.In one embodiment, the services platform 121 uses the output (e.g.physical divider predictions) of the physical divider platform 113 toprovide services such as navigation, mapping, other location-basedservices, etc.

In one embodiment, the physical divider platform 113 may be a platformwith multiple interconnected components. physical divider platform 113may include multiple servers, intelligent networking devices, computingdevices, components and corresponding software for providing parametricrepresentations of lane lines. In addition, it is noted that thephysical divider platform 113 may be a separate entity of the system100, a part of the one or more services 123, a part of the servicesplatform 121, or included within the vehicle 103 (e.g., a physicaldivider module 119).

In one embodiment, content providers 127 a-127 m (collectively referredto as content providers 127) may provide content or data (e.g.,including geographic data, parametric representations of mappedfeatures, etc.) to the geographic database 111, the physical dividerplatform 113, the services platform 121, the services 123, and thevehicle 103. The content provided may be any type of content, such asmap content, textual content, audio content, video content, imagecontent, etc. In one embodiment, the content providers 127 may providecontent that may aid in the detecting and classifying of physicaldividers or other related characteristics (e.g., OPPO, VRU, etc.). Inone embodiment, the content providers 127 may also store contentassociated with the geographic database 111, physical divider platform113, services platform 121, services 123, and/or vehicle 103. In anotherembodiment, the content providers 127 may manage access to a centralrepository of data, and offer a consistent, standard interface to data,such as a repository of the geographic database 111.

By way of example, the physical divider module 119 can be any type ofembedded system, mobile terminal, fixed terminal, or portable terminalincluding a built-in navigation system, a personal navigation device,mobile handset, station, unit, device, multimedia computer, multimediatablet, Internet node, communicator, desktop computer, laptop computer,notebook computer, netbook computer, tablet computer, personalcommunication system (PCS) device, personal digital assistants (PDAs),audio/video player, digital camera/camcorder, positioning device,fitness device, television receiver, radio broadcast receiver,electronic book device, game device, or any combination thereof,including the accessories and peripherals of these devices, or anycombination thereof. It is also contemplated that the physical dividermodule 119 can support any type of interface to the user (such as“wearable” circuitry, etc.). In one embodiment, the physical dividermodule 119 may be associated with the vehicle 103 or be a component partof the vehicle 103.

In one embodiment, the vehicle 103 is configured with various sensors117 for generating or collecting vehicular sensor data, relatedgeographic/map data, etc. In one embodiment, the sensed data representsensor data associated with a geographic location or coordinates atwhich the sensor data was collected. In this way, the sensor data canact as observation data that can be separated into location-awaretraining and evaluation datasets according to their data collectionlocations as well as used for detecting physical dividers according tothe embodiments described herein. By way of example, the sensors mayinclude a radar system, a LiDAR system, a global positioning sensor forgathering location data (e.g., GPS), a network detection sensor fordetecting wireless signals or receivers for different short-rangecommunications (e.g., Bluetooth, Wi-Fi, Li-Fi, near field communication(NFC) etc.), temporal information sensors, a camera/imaging sensor forgathering image data, an audio recorder for gathering audio data,velocity sensors mounted on steering wheels of the vehicles, switchsensors for determining whether one or more vehicle switches areengaged, and the like.

Other examples of sensors of the vehicle 103 may include light sensors,orientation sensors augmented with height sensors and accelerationsensor (e.g., an accelerometer can measure acceleration and can be usedto determine orientation of the vehicle), tilt sensors to detect thedegree of incline or decline of the vehicle along a path of travel,moisture sensors, pressure sensors, etc. In a further exampleembodiment, sensors about the perimeter of the vehicle 103 may detectthe relative distance of the vehicle from a physical divider, a lane orroadway, the presence of other vehicles, pedestrians, traffic lights,potholes and any other objects, or a combination thereof. In onescenario, the sensors may detect weather data, traffic information, or acombination thereof. In one embodiment, the vehicle 103 may include GPSor other satellite-based receivers to obtain geographic coordinates fromsatellites for determining current location and time. Further, thelocation can be determined by visual odometry, triangulation systemssuch as A-GPS, Cell of Origin, or other location extrapolationtechnologies. In yet another embodiment, the sensors can determine thestatus of various control elements of the car, such as activation ofwipers, use of a brake pedal, use of an acceleration pedal, angle of thesteering wheel, activation of hazard lights, activation of head lights,etc.

In one embodiment, the communication network 125 of system 100 includesone or more networks such as a data network, a wireless network, atelephony network, or any combination thereof. It is contemplated thatthe data network may be any local area network (LAN), metropolitan areanetwork (MAN), wide area network (WAN), a public data network (e.g., theInternet), short range wireless network, or any other suitablepacket-switched network, such as a commercially owned, proprietarypacket-switched network, e.g., a proprietary cable or fiber-opticnetwork, and the like, or any combination thereof. In addition, thewireless network may be, for example, a cellular network and may employvarious technologies including enhanced data rates for global evolution(EDGE), general packet radio service (GPRS), global system for mobilecommunications (GSM), Internet protocol multimedia subsystem (IMS),universal mobile telecommunications system (UMTS), etc., as well as anyother suitable wireless medium, e.g., worldwide interoperability formicrowave access (WiMAX), Long Term Evolution (LTE) networks, codedivision multiple access (CDMA), wideband code division multiple access(WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®,Internet Protocol (IP) data casting, satellite, mobile ad-hoc network(MANET), and the like, or any combination thereof.

By way of example, the physical divider platform 113, services platform121, services 123, vehicle 103, and/or content providers 127 communicatewith each other and other components of the system 100 using well known,new or still developing protocols. In this context, a protocol includesa set of rules defining how the network nodes within the communicationnetwork 125 interact with each other based on information sent over thecommunication links. The protocols are effective at different layers ofoperation within each node, from generating and receiving physicalsignals of various types, to selecting a link for transferring thosesignals, to the format of information indicated by those signals, toidentifying which software application executing on a computer systemsends or receives the information. The conceptually different layers ofprotocols for exchanging information over a network are described in theOpen Systems Interconnection (OSI) Reference Model.

Communications between the network nodes are typically effected byexchanging discrete packets of data. Each packet typically comprises (1)header information associated with a particular protocol, and (2)payload information that follows the header information and containsinformation that may be processed independently of that particularprotocol. In some protocols, the packet includes (3) trailer informationfollowing the payload and indicating the end of the payload information.The header includes information such as the source of the packet, itsdestination, the length of the payload, and other properties used by theprotocol. Often, the data in the payload for the particular protocolincludes a header and payload for a different protocol associated with adifferent, higher layer of the OSI Reference Model. The header for aparticular protocol typically indicates a type for the next protocolcontained in its payload. The higher layer protocol is said to beencapsulated in the lower layer protocol. The headers included in apacket traversing multiple heterogeneous networks, such as the Internet,typically include a physical (layer 1) header, a data-link (layer 2)header, an internetwork (layer 3) header and a transport (layer 4)header, and various application (layer 5, layer 6 and layer 7) headersas defined by the OSI Reference Model.

FIG. 12 is a diagram of a geographic database, according to oneembodiment. In one embodiment, the geographic database 111 includesgeographic data 1201 used for (or configured to be compiled to be usedfor) mapping and/or navigation-related services. In one embodiment,geographic features (e.g., two-dimensional or three-dimensionalfeatures) are represented using polygons (e.g., two-dimensionalfeatures) or polygon extrusions (e.g., three-dimensional features). Forexample, the edges of the polygons correspond to the boundaries or edgesof the respective geographic feature. In the case of a building, atwo-dimensional polygon can be used to represent a footprint of thebuilding, and a three-dimensional polygon extrusion can be used torepresent the three-dimensional surfaces of the building. It iscontemplated that although various embodiments are discussed withrespect to two-dimensional polygons, it is contemplated that theembodiments are also applicable to three-dimensional polygon extrusions.Accordingly, the terms polygons and polygon extrusions as used hereincan be used interchangeably.

In one embodiment, the following terminology applies to therepresentation of geographic features in the geographic database 111.

“Node”—A point that terminates a link.

“Line segment”—A straight line connecting two points.

“Link” (or “edge”)—A contiguous, non-branching string of one or moreline segments terminating in a node at each end.

“Shape point”—A point along a link between two nodes (e.g., used toalter a shape of the link without defining new nodes).

“Oriented link”—A link that has a starting node (referred to as the“reference node”) and an ending node (referred to as the “non referencenode”).

“Simple polygon”—An interior area of an outer boundary formed by astring of oriented links that begins and ends in one node. In oneembodiment, a simple polygon does not cross itself.

“Polygon”—An area bounded by an outer boundary and none or at least oneinterior boundary (e.g., a hole or island). In one embodiment, a polygonis constructed from one outer simple polygon and none or at least oneinner simple polygon. A polygon is simple if it just consists of onesimple polygon, or complex if it has at least one inner simple polygon.

In one embodiment, the geographic database 111 follows certainconventions. For example, links do not cross themselves and do not crosseach other except at a node. Also, there are no duplicated shape points,nodes, or links. Two links that connect each other have a common node.In the geographic database 111, overlapping geographic features arerepresented by overlapping polygons. When polygons overlap, the boundaryof one polygon crosses the boundary of the other polygon. In thegeographic database 111, the location at which the boundary of onepolygon intersects they boundary of another polygon is represented by anode. In one embodiment, a node may be used to represent other locationsalong the boundary of a polygon than a location at which the boundary ofthe polygon intersects the boundary of another polygon. In oneembodiment, a shape point is not used to represent a point at which theboundary of a polygon intersects the boundary of another polygon.

As shown, the geographic database 111 includes node data records 1203,road segment or link data records 1205, POI data records 1207, physicaldivider records 1209, other records 1211, and indexes 1213, for example.More, fewer or different data records can be provided. In oneembodiment, additional data records (not shown) can include cartographic(“carto”) data records, routing data, and maneuver data. In oneembodiment, the indexes 1213 may improve the speed of data retrievaloperations in the geographic database 111. In one embodiment, theindexes 1213 may be used to quickly locate data without having to searchevery row in the geographic database 111 every time it is accessed. Forexample, in one embodiment, the indexes 1213 can be a spatial index ofthe polygon points associated with stored feature polygons.

In exemplary embodiments, the road segment data records 1205 are linksor segments representing roads, streets, or paths, as can be used in thecalculated route or recorded route information for determination of oneor more personalized routes. The node data records 1203 are end pointscorresponding to the respective links or segments of the road segmentdata records 1205. The road link data records 1205 and the node datarecords 1203 represent a road network, such as used by vehicles, cars,and/or other entities. Alternatively, the geographic database 111 cancontain path segment and node data records or other data that representpedestrian paths or areas in addition to or instead of the vehicle roadrecord data, for example.

The road/link segments and nodes can be associated with attributes, suchas geographic coordinates, street names, address ranges, speed limits,turn restrictions at intersections, and other navigation relatedattributes, as well as POIs, such as gasoline stations, hotels,restaurants, museums, stadiums, offices, automobile dealerships, autorepair shops, buildings, stores, parks, etc. The geographic database 111can include data about the POIs and their respective locations in thePOI data records 1207. The geographic database 111 can also include dataabout places, such as cities, towns, or other communities, and othergeographic features, such as bodies of water, mountain ranges, etc. Suchplace or feature data can be part of the POI data records 1207 or can beassociated with POIs or POI data records 1207 (such as a data point usedfor displaying or representing a position of a city).

In one embodiment, the geographic database 111 can also include physicaldivider records 1209 for storing predicted physical dividers, OPPO, VRU,and/or other related road characteristics. The predicted data, forinstance, can be stored as attributes or data records of a physicaldivider overlay, which fuses with the predicted attributes with mapattributes or features. In one embodiment, the physical divider records1209 can be associated with segments of a road link (as opposed to anentire link). It is noted that the segmentation of the road for thepurposes of physical divider prediction can be different than the roadlink structure of the geographic database 111. In other words, thesegments can further subdivide the links of the geographic database 111into smaller segments (e.g., of uniform lengths such as 5-meters). Inthis way, physical dividers/OPPO/VRU can be predicted and represented ata level of granularity that is independent of the granularity or atwhich the actual road or road network is represented in the geographicdatabase 111. In one embodiment, the physical divider records 1209 canbe associated with one or more of the node records 1203, road segmentrecords 1205, and/or POI data records 1207; or portions thereof (e.g.,smaller or different segments than indicated in the road segment records1205) to provide situational awareness to drivers and provide for saferautonomous operation of vehicles. In this way, the predicted physicaldividers/OPPO/VRU/etc. stored in the physical divider records 1209 canalso be associated with the characteristics or metadata of thecorresponding records 1203, 1205, and/or 1207.

In one embodiment, the geographic database 111 can be maintained by thecontent provider 127 in association with the services platform 121(e.g., a map developer). The map developer can collect geographic datato generate and enhance the geographic database 111. There can bedifferent ways used by the map developer to collect data. These ways caninclude obtaining data from other sources, such as municipalities orrespective geographic authorities. In addition, the map developer canemploy field personnel to travel by vehicle along roads throughout thegeographic region to observe features (e.g., physical dividers, OPPO,VRU, etc.) and/or record information about them, for example. Also,remote sensing, such as aerial or satellite photography, can be used.

In one embodiment, the geographic database 111 include high resolutionor high definition (HD) mapping data that provide centimeter-level orbetter accuracy of map features. For example, the geographic database111 can be based on Light Detection and Ranging (LiDAR) or equivalenttechnology to collect billions of 3D points and model road surfaces andother map features down to the number lanes and their widths. In oneembodiment, the HD mapping data capture and store details such as theslope and curvature of the road, lane markings, roadside objects such assign posts, including what the signage denotes. By way of example, theHD mapping data enable highly automated vehicles to precisely localizethemselves on the road, and to determine road attributes (e.g., learnedspeed limit values) to at high accuracy levels.

In one embodiment, the geographic database 111 is stored as ahierarchical or multi-level tile-based projection or structure. Morespecifically, in one embodiment, the geographic database 111 may bedefined according to a normalized Mercator projection. Other projectionsmay be used. By way of example, the map tile grid of a Mercator orsimilar projection is a multilevel grid. Each cell or tile in a level ofthe map tile grid is divisible into the same number of tiles of thatsame level of grid. In other words, the initial level of the map tilegrid (e.g., a level at the lowest zoom level) is divisible into fourcells or rectangles. Each of those cells are in turn divisible into fourcells, and so on until the highest zoom or resolution level of theprojection is reached.

In one embodiment, the map tile grid may be numbered in a systematicfashion to define a tile identifier (tile ID). For example, the top lefttile may be numbered 00, the top right tile may be numbered 01, thebottom left tile may be numbered 10, and the bottom right tile may benumbered 11. In one embodiment, each cell is divided into fourrectangles and numbered by concatenating the parent tile ID and the newtile position. A variety of numbering schemes also is possible. Anynumber of levels with increasingly smaller geographic areas mayrepresent the map tile grid. Any level (n) of the map tile grid has2(n+1) cells. Accordingly, any tile of the level (n) has a geographicarea of A/2(n+1) where A is the total geographic area of the world orthe total area of the map tile grid 10. Because of the numbering system,the exact position of any tile in any level of the map tile grid orprojection may be uniquely determined from the tile ID.

In one embodiment, the system 100 may identify a tile by a quadkeydetermined based on the tile ID of a tile of the map tile grid. Thequadkey, for example, is a one-dimensional array including numericalvalues. In one embodiment, the quadkey may be calculated or determinedby interleaving the bits of the row and column coordinates of a tile inthe grid at a specific level. The interleaved bits may be converted to apredetermined base number (e.g., base 10, base 4, hexadecimal). In oneexample, leading zeroes are inserted or retained regardless of the levelof the map tile grid in order to maintain a constant length for theone-dimensional array of the quadkey. In another example, the length ofthe one-dimensional array of the quadkey may indicate the correspondinglevel within the map tile grid 10. In one embodiment, the quadkey is anexample of the hash or encoding scheme of the respective geographicalcoordinates of a geographical data point that can be used to identify atile in which the geographical data point is located.

The geographic database 111 can be a master geographic database storedin a format that facilitates updating, maintenance, and development. Forexample, the master geographic database or data in the master geographicdatabase can be in an Oracle spatial format or other spatial format,such as for development or production purposes. The Oracle spatialformat or development/production database can be compiled into adelivery format, such as a geographic data files (GDF) format. The datain the production and/or delivery formats can be compiled or furthercompiled to form geographic database products or databases, which can beused in end user navigation devices or systems.

For example, geographic data is compiled (such as into a platformspecification format (PSF) format) to organize and/or configure the datafor performing navigation-related functions and/or services, such asroute calculation, route guidance, map display, speed calculation,distance and travel time functions, and other functions, by a navigationdevice, such as by the vehicle 103, for example. The navigation-relatedfunctions can correspond to vehicle navigation, pedestrian navigation,or other types of navigation. The compilation to produce the end userdatabases can be performed by a party or entity separate from the mapdeveloper. For example, a customer of the map developer, such as anavigation device developer or other end user device developer, canperform compilation on a received geographic database in a deliveryformat to produce one or more compiled navigation databases.

The processes described herein for detecting physical dividers on a roadsegment may be advantageously implemented via software, hardware (e.g.,general processor, Digital Signal Processing (DSP) chip, an ApplicationSpecific Integrated Circuit (ASIC), Field Programmable Gate Arrays(FPGAs), etc.), firmware or a combination thereof. Such exemplaryhardware for performing the described functions is detailed below.

FIG. 13 illustrates a computer system 1300 upon which an embodiment maybe implemented. Computer system 1300 is programmed (e.g., via computerprogram code or instructions) to detect physical dividers on a roadsegment as described herein and includes a communication mechanism suchas a bus 1310 for passing information between other internal andexternal components of the computer system 1300. Information (alsocalled data) is represented as a physical expression of a measurablephenomenon, typically electric voltages, but including, in otherembodiments, such phenomena as magnetic, electromagnetic, pressure,chemical, biological, molecular, atomic, sub-atomic and quantuminteractions. For example, north and south magnetic fields, or a zeroand non-zero electric voltage, represent two states (0, 1) of a binarydigit (bit). Other phenomena can represent digits of a higher base. Asuperposition of multiple simultaneous quantum states before measurementrepresents a quantum bit (qubit). A sequence of one or more digitsconstitutes digital data that is used to represent a number or code fora character. In some embodiments, information called analog data isrepresented by a near continuum of measurable values within a particularrange.

A bus 1310 includes one or more parallel conductors of information sothat information is transferred quickly among devices coupled to the bus1310. One or more processors 1302 for processing information are coupledwith the bus 1310.

A processor 1302 performs a set of operations on information asspecified by computer program code related to detecting physicaldividers on a road segment. The computer program code is a set ofinstructions or statements providing instructions for the operation ofthe processor and/or the computer system to perform specified functions.The code, for example, may be written in a computer programming languagethat is compiled into a native instruction set of the processor. Thecode may also be written directly using the native instruction set(e.g., machine language). The set of operations include bringinginformation in from the bus 1310 and placing information on the bus1310. The set of operations also typically include comparing two or moreunits of information, shifting positions of units of information, andcombining two or more units of information, such as by addition ormultiplication or logical operations like OR, exclusive OR (XOR), andAND. Each operation of the set of operations that can be performed bythe processor is represented to the processor by information calledinstructions, such as an operation code of one or more digits. Asequence of operations to be executed by the processor 1302, such as asequence of operation codes, constitute processor instructions, alsocalled computer system instructions or, simply, computer instructions.Processors may be implemented as mechanical, electrical, magnetic,optical, chemical or quantum components, among others, alone or incombination.

Computer system 1300 also includes a memory 1304 coupled to bus 1310.The memory 1304, such as a random access memory (RAM) or other dynamicstorage device, stores information including processor instructions fordetecting physical dividers on a road segment. Dynamic memory allowsinformation stored therein to be changed by the computer system 1300.RAM allows a unit of information stored at a location called a memoryaddress to be stored and retrieved independently of information atneighboring addresses. The memory 1304 is also used by the processor1302 to store temporary values during execution of processorinstructions. The computer system 1300 also includes a read only memory(ROM) 1306 or other static storage device coupled to the bus 1310 forstoring static information, including instructions, that is not changedby the computer system 1300. Some memory is composed of volatile storagethat loses the information stored thereon when power is lost. Alsocoupled to bus 1310 is a non-volatile (persistent) storage device 1308,such as a magnetic disk, optical disk or flash card, for storinginformation, including instructions, that persists even when thecomputer system 1300 is turned off or otherwise loses power.

Information, including instructions for detecting physical dividers on aroad segment, is provided to the bus 1310 for use by the processor froman external input device 1312, such as a keyboard containingalphanumeric keys operated by a human user, or a sensor. A sensordetects conditions in its vicinity and transforms those detections intophysical expression compatible with the measurable phenomenon used torepresent information in computer system 1300. Other external devicescoupled to bus 1310, used primarily for interacting with humans, includea display device 1314, such as a cathode ray tube (CRT) or a liquidcrystal display (LCD), or plasma screen or printer for presenting textor images, and a pointing device 1316, such as a mouse or a trackball orcursor direction keys, or motion sensor, for controlling a position of asmall cursor image presented on the display 1314 and issuing commandsassociated with graphical elements presented on the display 1314. Insome embodiments, for example, in embodiments in which the computersystem 1300 performs all functions automatically without human input,one or more of external input device 1312, display device 1314 andpointing device 1316 is omitted.

In the illustrated embodiment, special purpose hardware, such as anapplication specific integrated circuit (ASIC) 1320, is coupled to bus1310. The special purpose hardware is configured to perform operationsnot performed by processor 1302 quickly enough for special purposes.Examples of application specific ICs include graphics accelerator cardsfor generating images for display 1314, cryptographic boards forencrypting and decrypting messages sent over a network, speechrecognition, and interfaces to special external devices, such as roboticarms and medical scanning equipment that repeatedly perform some complexsequence of operations that are more efficiently implemented inhardware.

Computer system 1300 also includes one or more instances of acommunications interface 1370 coupled to bus 1310. Communicationinterface 1370 provides a one-way or two-way communication coupling to avariety of external devices that operate with their own processors, suchas printers, scanners and external disks. In general, the coupling iswith a network link 1378 that is connected to a local network 1380 towhich a variety of external devices with their own processors areconnected. For example, communication interface 1370 may be a parallelport or a serial port or a universal serial bus (USB) port on a personalcomputer. In some embodiments, communications interface 1370 is anintegrated services digital network (ISDN) card or a digital subscriberline (DSL) card or a telephone modem that provides an informationcommunication connection to a corresponding type of telephone line. Insome embodiments, a communication interface 1370 is a cable modem thatconverts signals on bus 1310 into signals for a communication connectionover a coaxial cable or into optical signals for a communicationconnection over a fiber optic cable. As another example, communicationsinterface 1370 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN, such as Ethernet. Wirelesslinks may also be implemented. For wireless links, the communicationsinterface 1370 sends or receives or both sends and receives electrical,acoustic or electromagnetic signals, including infrared and opticalsignals, that carry information streams, such as digital data. Forexample, in wireless handheld devices, such as mobile telephones likecell phones, the communications interface 1370 includes a radio bandelectromagnetic transmitter and receiver called a radio transceiver. Incertain embodiments, the communications interface 1370 enablesconnection to the communication network 125 for detecting physicaldividers on a road segment.

The term computer-readable medium is used herein to refer to any mediumthat participates in providing information to processor 1302, includinginstructions for execution. Such a medium may take many forms,including, but not limited to, non-volatile media, volatile media andtransmission media. Non-volatile media include, for example, optical ormagnetic disks, such as storage device 1308. Volatile media include, forexample, dynamic memory 1304. Transmission media include, for example,coaxial cables, copper wire, fiber optic cables, and carrier waves thattravel through space without wires or cables, such as acoustic waves andelectromagnetic waves, including radio, optical and infrared waves.Signals include man-made transient variations in amplitude, frequency,phase, polarization or other physical properties transmitted through thetransmission media. Common forms of computer-readable media include, forexample, a floppy disk, a flexible disk, hard disk, magnetic tape, anyother magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium,punch cards, paper tape, optical mark sheets, any other physical mediumwith patterns of holes or other optically recognizable indicia, a RAM, aPROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, acarrier wave, or any other medium from which a computer can read.

FIG. 14 illustrates a chip set 1400 upon which an embodiment may beimplemented. Chip set 1400 is programmed to detect physical dividers ona road segment as described herein and includes, for instance, theprocessor and memory components described with respect to FIG. 13incorporated in one or more physical packages (e.g., chips). By way ofexample, a physical package includes an arrangement of one or morematerials, components, and/or wires on a structural assembly (e.g., abaseboard) to provide one or more characteristics such as physicalstrength, conservation of size, and/or limitation of electricalinteraction. It is contemplated that in certain embodiments the chip setcan be implemented in a single chip.

In one embodiment, the chip set 1400 includes a communication mechanismsuch as a bus 1401 for passing information among the components of thechip set 1400. A processor 1403 has connectivity to the bus 1401 toexecute instructions and process information stored in, for example, amemory 1405. The processor 1403 may include one or more processing coreswith each core configured to perform independently. A multi-coreprocessor enables multiprocessing within a single physical package.Examples of a multi-core processor include two, four, eight, or greaternumbers of processing cores. Alternatively or in addition, the processor1403 may include one or more microprocessors configured in tandem viathe bus 1401 to enable independent execution of instructions,pipelining, and multithreading. The processor 1403 may also beaccompanied with one or more specialized components to perform certainprocessing functions and tasks such as one or more digital signalprocessors (DSP) 1407, or one or more application-specific integratedcircuits (ASIC) 1409. A DSP 1407 typically is configured to processreal-world signals (e.g., sound) in real time independently of theprocessor 1403. Similarly, an ASIC 1409 can be configured to performedspecialized functions not easily performed by a general purposedprocessor. Other specialized components to aid in performing theinventive functions described herein include one or more fieldprogrammable gate arrays (FPGA) (not shown), one or more controllers(not shown), or one or more other special-purpose computer chips.

The processor 1403 and accompanying components have connectivity to thememory 1405 via the bus 1401. The memory 1405 includes both dynamicmemory (e.g., RAM, magnetic disk, writable optical disk, etc.) andstatic memory (e.g., ROM, CD-ROM, etc.) for storing executableinstructions that when executed perform the inventive steps describedherein to detect physical dividers on a road segment. The memory 1405also stores the data associated with or generated by the execution ofthe inventive steps.

FIG. 15 is a diagram of exemplary components of a mobile terminal 1501(e.g., handset, vehicle or part thereof, etc.) capable of operating inthe system of FIG. 1, according to one embodiment. Generally, a radioreceiver is often defined in terms of front-end and back-endcharacteristics. The front-end of the receiver encompasses all of theRadio Frequency (RF) circuitry whereas the back-end encompasses all ofthe base-band processing circuitry. Pertinent internal components of thetelephone include a Main Control Unit (MCU) 1503, a Digital SignalProcessor (DSP) 1505, and a receiver/transmitter unit including amicrophone gain control unit and a speaker gain control unit. A maindisplay unit 1507 provides a display to the user in support of variousapplications and mobile station functions that offer automatic contactmatching. An audio function circuitry 1509 includes a microphone 1511and microphone amplifier that amplifies the speech signal output fromthe microphone 1511. The amplified speech signal output from themicrophone 1511 is fed to a coder/decoder (CODEC) 1513.

A radio section 1515 amplifies power and converts frequency in order tocommunicate with a base station, which is included in a mobilecommunication system, via antenna 1517. The power amplifier (PA) 1519and the transmitter/modulation circuitry are operationally responsive tothe MCU 1503, with an output from the PA 1519 coupled to the duplexer1521 or circulator or antenna switch, as known in the art. The PA 1519also couples to a battery interface and power control unit 1520.

In use, a user of mobile station 1501 speaks into the microphone 1511and his or her voice along with any detected background noise isconverted into an analog voltage. The analog voltage is then convertedinto a digital signal through the Analog to Digital Converter (ADC)1523. The control unit 1503 routes the digital signal into the DSP 1505for processing therein, such as speech encoding, channel encoding,encrypting, and interleaving. In one embodiment, the processed voicesignals are encoded, by units not separately shown, using a cellulartransmission protocol such as global evolution (EDGE), general packetradio service (GPRS), global system for mobile communications (GSM),Internet protocol multimedia subsystem (IMS), universal mobiletelecommunications system (UMTS), etc., as well as any other suitablewireless medium, e.g., microwave access (WiMAX), Long Term Evolution(LTE) networks, code division multiple access (CDMA), wireless fidelity(WiFi), satellite, and the like.

The encoded signals are then routed to an equalizer 1525 forcompensation of any frequency-dependent impairments that occur duringtransmission though the air such as phase and amplitude distortion.After equalizing the bit stream, the modulator 1527 combines the signalwith a RF signal generated in the RF interface 1529. The modulator 1527generates a sine wave by way of frequency or phase modulation. In orderto prepare the signal for transmission, an up-converter 1531 combinesthe sine wave output from the modulator 1527 with another sine wavegenerated by a synthesizer 1533 to achieve the desired frequency oftransmission. The signal is then sent through a PA 1519 to increase thesignal to an appropriate power level. In practical systems, the PA 1519acts as a variable gain amplifier whose gain is controlled by the DSP1505 from information received from a network base station. The signalis then filtered within the duplexer 1521 and optionally sent to anantenna coupler 1535 to match impedances to provide maximum powertransfer. Finally, the signal is transmitted via antenna 1517 to a localbase station. An automatic gain control (AGC) can be supplied to controlthe gain of the final stages of the receiver. The signals may beforwarded from there to a remote telephone which may be another cellulartelephone, other mobile phone or a land-line connected to a PublicSwitched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile station 1501 are received viaantenna 1517 and immediately amplified by a low noise amplifier (LNA)1537. A down-converter 1539 lowers the carrier frequency while thedemodulator 1541 strips away the RF leaving only a digital bit stream.The signal then goes through the equalizer 1525 and is processed by theDSP 1505. A Digital to Analog Converter (DAC) 1543 converts the signaland the resulting output is transmitted to the user through the speaker1545, all under control of a Main Control Unit (MCU) 1503—which can beimplemented as a Central Processing Unit (CPU) (not shown).

The MCU 1503 receives various signals including input signals from thekeyboard 1547. The keyboard 1547 and/or the MCU 1503 in combination withother user input components (e.g., the microphone 1511) comprise a userinterface circuitry for managing user input. The MCU 1503 runs a userinterface software to facilitate user control of at least some functionsof the mobile station 1501 to detect physical dividers on a roadsegment. The MCU 1503 also delivers a display command and a switchcommand to the display 1507 and to the speech output switchingcontroller, respectively. Further, the MCU 1503 exchanges informationwith the DSP 1505 and can access an optionally incorporated SIM card1549 and a memory 1551. In addition, the MCU 1503 executes variouscontrol functions required of the station. The DSP 1505 may, dependingupon the implementation, perform any of a variety of conventionaldigital processing functions on the voice signals. Additionally, DSP1505 determines the background noise level of the local environment fromthe signals detected by microphone 1511 and sets the gain of microphone1511 to a level selected to compensate for the natural tendency of theuser of the mobile station 1501.

The CODEC 1513 includes the ADC 1523 and DAC 1543. The memory 1551stores various data including call incoming tone data and is capable ofstoring other data including music data received via, e.g., the globalInternet. The software module could reside in RAM memory, flash memory,registers, or any other form of writable computer-readable storagemedium known in the art including non-transitory computer-readablestorage medium. For example, the memory device 1551 may be, but notlimited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage,or any other non-volatile or non-transitory storage medium capable ofstoring digital data.

An optionally incorporated SIM card 1549 carries, for instance,important information, such as the cellular phone number, the carriersupplying service, subscription details, and security information. TheSIM card 1549 serves primarily to identify the mobile station 1501 on aradio network. The card 1549 also contains a memory for storing apersonal telephone number registry, text messages, and user specificmobile station settings.

While the invention has been described in connection with a number ofembodiments and implementations, the invention is not so limited butcovers various obvious modifications and equivalent arrangements, whichfall within the purview of the appended claims. Although features of theinvention are expressed in certain combinations among the claims, it iscontemplated that these features can be arranged in any combination andorder.

What is claimed is:
 1. A computer-implemented method for detecting apresence of a physical divider on a road segment comprising: determininga number of positive observations of the physical divider on the roadsegment based on sensor data collected from one or more vehiclestraveling the road segment; determining a number of negativeobservations of the physical divider on the road segment based on thesensor data; calculating a probability of the presence of the physicaldivider based on the number of the positive observations and the numberof the negative observations; and updating map data for the road segmentto indicate the presence of the physical divider based on determiningthat the probability of the presence of the physical divider is greaterthan a threshold value.
 2. The method of claim 1, further comprising:calculating the threshold value based on ground truth data indicating aknown presence, a known absence, or a combination thereof of a pluralityof reference physical dividers on a plurality of reference roadsegments.
 3. The method of claim 1, further comprising: filtering thepositive observations, the negative observations, or a combinationthereof based on determining that the one or more vehicles are fartherthan a distance threshold from the physical divider on the road segment,wherein the number of the positive observations, the number of thenegative observations, or a combination thereof is determined based onthe filtered positive observations, the filtered negative observations,or a combination thereof.
 4. The method of claim 3, wherein the distancethreshold is based on physical dimensions of a travel lane adjacent tothe physical divider.
 5. The method of claim 1, wherein the sensor datacomprises a plurality of probe trajectories including a plurality ofphysical divider flags sequenced in time.
 6. The method of claim 5,wherein the positive observations are extracted by segmenting a portionof the plurality of probe trajectories that corresponds to the roadsegment, and wherein the portion begins with a positive flag and endswith a negative flag as determined from the plurality of physicaldivider flags.
 7. The method of claim 5, wherein the negativeobservations are extracted by segmenting a portion of the plurality ofprobe trajectories that corresponds to the road segment, and wherein theportion begins with a negative flag and ends with a negative positivedivider flag as determined from the plurality of physical divider flags.8. The method of claim 1, wherein the road segment is one segment of aplurality of segments of a road link, and wherein the presence of thephysical divider for each of the plurality of segments is determinedindependently.
 9. The method of claim 1, wherein the presence of thephysical divider is determined independently for a left side, a rightside, or a combination thereof of the road segment.
 10. An apparatus fordetecting a presence of a physical divider on a road segment comprising:at least one processor; and at least one memory including computerprogram code for one or more programs, the at least one memory and thecomputer program code configured to, with the at least one processor,cause the apparatus to perform at least the following, determine anumber of positive observations of the physical divider on the roadsegment and a number of negative observations of the physical divider onthe road segment; calculate a probability of the presence of thephysical divider based on the number of the positive observations andthe number of the negative observations; and update map data for theroad segment to indicate the presence of the physical divider based ondetermining that the probability of the presence of the physical divideris greater than a threshold value.
 11. The apparatus of claim 10,wherein the apparatus is further caused to: calculate the thresholdvalue based on ground truth data indicating a known presence, a knownabsence, or a combination thereof of a plurality of reference physicaldividers on a plurality of reference road segments.
 12. The apparatus ofclaim 10, wherein the apparatus if further caused to: filter thepositive observations, the negative observations, or a combinationthereof based on determining that one or more corresponding vehicles arefarther than a distance threshold from the physical divider on the roadsegment when the positive observations, the negative observations, or acombination thereof were collected, wherein the number of the positiveobservations, the number of the negative observations, or a combinationthereof is determined based on the filtered positive observations, thefiltered negative observations, or a combination thereof.
 13. Theapparatus of claim 10, wherein the number of the positive observationsand the number of the negative observations are based on sensor datacollected from one or more vehicles traveling the road segment, andwherein the sensor data comprises a plurality of probe trajectoriesincluding a plurality of physical divider flags sequenced in time. 14.The apparatus of claim 13, wherein the positive observations areextracted by segmenting a portion of the plurality of probe trajectoriesthat corresponds to the road segment, and wherein the portion beginswith a positive flag and ends with a negative flag as determined fromthe plurality of physical divider flags.
 15. The apparatus of claim 13,wherein the negative observations are extracted by segmenting a portionof the plurality of probe trajectories that corresponds to the roadsegment, and wherein the portion begins with a negative flag and endswith a negative positive divider flag as determined from the pluralityof physical divider flags.
 16. A non-transitory computer-readablestorage medium detecting a presence of a physical divider on a roadsegment, carrying one or more sequences of one or more instructionswhich, when executed by one or more processors, cause an apparatus toperform: determining a number of positive observations of the physicaldivider on the road segment and a number of negative observations of thephysical divider on the road segment; calculating a probability of thepresence of the physical divider based on the number of the positiveobservations and the number of the negative observations; and updatingmap data for the road segment to indicate the presence of the physicaldivider based on determining that the probability of the presence of thephysical divider is greater than a threshold value.
 17. Thenon-transitory computer-readable storage medium of claim 16, whereinprobability of the presence of the physical divider is calculated basedon a ratio of the number of positive observations to a total number ofthe positive observations and the negative observations.
 18. Thenon-transitory computer-readable storage medium of claim 16, wherein theapparatus is caused to further perform: calculating the threshold valuebased on ground truth data comprising a data on a known presence, aknown absence, or a combination thereof of a plurality of referencephysical dividers on a plurality of reference road segments.
 19. Thenon-transitory computer-readable storage medium of claim 16, wherein theroad segment is one segment of a plurality of segments of a road link,and wherein the presence of the physical divider for each of theplurality of segments is determined independently.
 20. Thenon-transitory computer-readable storage medium of claim 16, wherein thepresence of the physical divider is determined independently for a leftside, a right side, or a combination thereof of the road segment.